Statistical Programming for Adaptive Monitoring in Software Defined Networks using Linear Programming

Authors

Fatemeh Amou Aghaei, TU DublinFollow
Ruairí de Fréin, Technological University DublinFollow

Author ORCID Identifier

0000-0002-2844-1823

Document Type

Conference Paper

Disciplines

Statistics

Publication Details

Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.

Abstract

Adaptive monitoring in Software Defined Networks (SDNs) is essential to reduce overhead and prioritize critical flows. This paper introduces AdaptMon, a Linear Programming-based model that dynamically allocates monitoring resources based on estimated error rates. By modeling allocation as a probability distribution and enforcing a fairness constraint using an ℓ1-style deviation bound, the approach maximizes expected monitoring utility while preserving balance across the network. Simulations show that AdaptMon reduces monitoring delay by up to 40% without sacrificing anomaly detection accuracy. The model is interpretable, lightweight, and grounded in statistical programming, making it a practical solution for real-time SDN environments.

DOI

https://doi.org/10.21427/s1ds-r495

Recommended Citation

Amou Aghaei, Fatemeh and de Fréin, Ruairí, "Statistical Programming for Adaptive Monitoring in Software Defined Networks using Linear Programming" (2025). SAML-25 Workshop on Statistical and Machine Learning. 19.
https://arrow.tudublin.ie/saml/19

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.